My Jump 2应用程序测量大学生下肢垂直刚度的信度和效度

背景:证实My Jump 2应用程序测量下肢垂直刚度的信效度水平,可为其作为Kistler三维测力台测量下肢垂直刚度的替代方法提供可能性。目的:验证My Jump 2应用程序测量大学生下肢垂直刚度的信效度。方法:通过Kistler三维测力台和My Jump 2应用程序采集受试者的跳深数据并计算受试者的下肢垂直刚度。采用组内相关系数分析My Jump 2应用程序与Kistler三维测力台测得的数据,以验证My Jump 2应用程序的信度。将2个设备之间的偏倚值和平均值绘制成Bland-Altman图,验证2种测试方法之间的一致性。最后,使用克朗巴哈系数(α)和变异系数分析My Jump 2应用程序在30,40 cm高度的重测信度,使用皮尔逊积矩相关系数分析My Jump 2应用程序的相关性。结果与结论:①My Jump 2应用程序在测量下肢垂直刚度时具有较高的信效度;②同时由于其具有低成本、携带便捷和可用于大样本的场地测试的优势,可作为测力台的替代工具在大学生以及类似人群中对下肢垂直刚度进行测试。

A fringe jump counting method for the phase measurement in the HCN laser interferometer on EAST and its FPGA-based implementation

Electron density in fusion plasma is usually diagnosed using laser-aided interferometers. The phase difference signal obtained after phase demodulation is wrapped, which is also called a fringe jump. A method has been developed to unwrap the phase difference signal in real time using FPGA, specifically designed to handle fringe jumps in the hydrogen cyanide(HCN) laser interferometer on the EAST superconducting tokamak. This method is designed for a phase demodulator using the fast Fourier transform(FFT) method at the front end. The method is better adapted for hardware implementation compared to complex mathematical analysis algorithms, such as field programmable gate array(FPGA). It has been applied to process the phase measurement results of the HCN laser interferometer on EAST in real time. Electron density results show good confidence in the fringe jump unwrapping method. Further possible application in other laser interferometers, such as the POlarimeter-INTerferometer(POINT)system on EAST tokamak is also discussed.

Fault Estimation for a Class of Markov Jump Piecewise-Affine Systems: Current Feedback Based Iterative Learning Approach

In this paper, the issues of stochastic stability analysis and fault estimation are investigated for a class of continuoustime Markov jump piecewise-affine(PWA) systems against actuator and sensor faults. Firstly, a novel mode-dependent PWA iterative learning observer with current feedback is designed to estimate the system states and faults, simultaneously, which contains both the previous iteration information and the current feedback mechanism. The auxiliary feedback channel optimizes the response speed of the observer, therefore the estimation error would converge to zero rapidly. Then, sufficient conditions for stochastic stability with guaranteed performance are demonstrated for the estimation error system, and the equivalence relations between the system information and the estimated information can be established via iterative accumulating representation.Finally, two illustrative examples containing a class of tunnel diode circuit systems are presented to fully demonstrate the effectiveness and superiority of the proposed iterative learning observer with current feedback.

Study of a Hydraulic Jump in an Asymmetric Trapezoidal Channel with Different Sluice Gates

In this study,the main properties of the hydraulic jump in an asymmetric trapezoidal flume are analyzed experimentally,including the so-called sequent depths,characteristic lengths,and efficiency.In particular,an asymmetric trapezoidal flume with a length of 7 m and a width of 0.304 m is considered,with the bottom of the flume transversely inclined at an angle of m=0.296 and vertical lateral sides.The corresponding inflow Froude number is allowed to range in the interval(1.40<F1<6.11).The properties of this jump are compared to those of hydraulic jumps in channels with other types of cross-sections.A relationship for calculating hydraulic jump efficiency is proposed for the considered flume.For F1>5,the hydraulic jump is found to be more effective than that occurring in triangular and symmetric trapezoidal channels.Also,when■mes>8 and■>5,the hydraulic jump in the asymmetrical trapezoidal channel downstream of a parallelogram sluice gate is completely formed as opposed to the situation where a triangular sluice is considered.

Prediction of the undrained shear strength of remolded soil with non-linear regression,fuzzy logic,and artificial neural network

This study aims to predict the undrained shear strength of remolded soil samples using non-linear regression analyses,fuzzy logic,and artificial neural network modeling.A total of 1306 undrained shear strength results from 230 different remolded soil test settings reported in 21 publications were collected,utilizing six different measurement devices.Although water content,plastic limit,and liquid limit were used as input parameters for fuzzy logic and artificial neural network modeling,liquidity index or water content ratio was considered as an input parameter for non-linear regression analyses.In non-linear regression analyses,12 different regression equations were derived for the prediction of undrained shear strength of remolded soil.Feed-Forward backpropagation and the TANSIG transfer function were used for artificial neural network modeling,while the Mamdani inference system was preferred with trapezoidal and triangular membership functions for fuzzy logic modeling.The experimental results of 914 tests were used for training of the artificial neural network models,196 for validation and 196 for testing.It was observed that the accuracy of the artificial neural network and fuzzy logic modeling was higher than that of the non-linear regression analyses.Furthermore,a simple and reliable regression equation was proposed for assessments of undrained shear strength values with higher coefficients of determination.

Asynchronous Learning-Based Output Feedback Sliding Mode Control for Semi-Markov Jump Systems: A Descriptor Approach

This paper presents an asynchronous output-feed-back control strategy of semi-Markovian systems via sliding mode-based learning technique.Compared with most literature results that require exact prior knowledge of system state and mode information,an asynchronous output-feedback sliding sur-face is adopted in the case of incompletely available state and non-synchronization phenomenon.The holonomic dynamics of the sliding mode are characterized by a descriptor system in which the switching surface is regarded as the fast subsystem and the system dynamics are viewed as the slow subsystem.Based upon the co-occurrence of two subsystems,the sufficient stochastic admissibility criterion of the holonomic dynamics is derived by utilizing the characteristics of cumulative distribution functions.Furthermore,a recursive learning controller is formulated to guarantee the reachability of the sliding manifold and realize the chattering reduction of the asynchronous switching and sliding motion.Finally,the proposed theoretical method is substantia-ted through two numerical simulations with the practical contin-uous stirred tank reactor and F-404 aircraft engine model,respectively.

Modified filter for mean elements estimation with state jumping

To investigate the real-time mean orbital elements(MOEs)estimation problem under the influence of state jumping caused by non-fatal spacecraft collision or protective orbit trans-fer,a modified augmented square-root unscented Kalman filter(MASUKF)is proposed.The MASUKF is composed of sigma points calculation,time update,modified state jumping detec-tion,and measurement update.Compared with the filters used in the existing literature on MOEs estimation,it has three main characteristics.Firstly,the state vector is augmented from six to nine by the added thrust acceleration terms,which makes the fil-ter additionally give the state-jumping-thrust-acceleration esti-mation.Secondly,the normalized innovation is used for state jumping detection to set detection threshold concisely and make the filter detect various state jumping with low latency.Thirdly,when sate jumping is detected,the covariance matrix inflation will be done,and then an extra time update process will be con-ducted at this time instance before measurement update.In this way,the relatively large estimation error at the detection moment can significantly decrease.Finally,typical simulations are per-formed to illustrated the effectiveness of the method.

Inverse Spectral Problem for Sturm-Liouville Operator with Boundary and Jump Conditions Dependent on the Spectral Parameter

In this paper, the inverse spectral problem of Sturm-Liouville operator with boundary conditions and jump conditions dependent on the spectral parameter is investigated. Firstly, the self-adjointness of the problem and the eigenvalue properties are given, then the asymptotic formulas of eigenvalues and eigenfunctions are presented. Finally, the uniqueness theorems of the corresponding inverse problems are given by Weyl function theory and inverse spectral data approach.

基于Jump-SBERT的二进制代码相似性检测技术研究

二进制代码相似性检测技术在不同的安全领域中有着重要的作用。针对现有的二进制代码相似性检测方法面临计算开销大且精度低、二进制函数语义信息识别不全面和评估数据集单一等问题,提出了一种基于Jump-SBERT的二进制代码相似性检测技术。Jump-SBERT有两个主要创新点,一是利用孪生网络构建SBERT网络结构,该网络结构能够在降低模型的计算开销的同时保持计算精度不变;二是引入了跳转识别机制,使Jump-SBERT可以学习到二进制函数的图结构信息,从而更加全面地捕获二进制函数的语义信息。实验结果表明,Jump-SBERT在小函数池(32个函数)中的识别准确率可达96.3%,在大函数池(10000个函数)中的识别准确率可达85.1%,比最先进(State-of-the-Art,SOTA)的方法高出36.13%,且Jump-SBERT在大规模二进制代码相似性检测中的表现更加稳定。消融实验表明,两个主要创新点对Jump-SBERT均有积极作用,其中,跳转识别机制的贡献最高可达9.11%。

Microsphere femtosecond laser sub-50 nm structuring in far field via non-linear absorption

Creation of arbitrary features with high resolution is critically important in the fabrication of nano-optoelectronic devices.Here,sub-50 nm surface structuring is achieved directly on Sb2S3 thin films via microsphere femtosecond laser irradi-ation in far field.By varying laser fluence and scanning speed,nano-feature sizes can be flexibly tuned.Such small patterns are attributed to the co-effect of microsphere focusing,two-photons absorption,top threshold effect,and high-repetition-rate femtosecond laser-induced incubation effect.The minimum feature size can be reduced down to~30 nm(λ/26)by manipulating film thickness.The fitting analysis between the ablation width and depth predicts that the feature size can be down to~15 nm at the film thickness of~10 nm.A nano-grating is fabricated,which demonstrates desirable beam diffraction performance.This nano-scale resolution would be highly attractive for next-generation laser nano-lithography in far field and in ambient air.

The effects of plyometric jump training on lower-limb stiffness in healthy individuals:A meta-analytical comparison

Purpose:This study aimed to examine the effects of plyometric jump training(PJT)on lower-limb stiffness.Methods:Systematic searches were conducted in PubMed,Web of Science,and Scopus.Study participants included healthy males and females who undertook a PJT programme isolated from any other training type.Results:There was a small effect size(ES)of PJT on lower-limb stiffness(ES=0.33,95%confidence interval(95%CI):0.07-0.60,z=2.47,p=0.01).Untrained individuals exhibited a larger ES(ES=0.46,95%CI:0.08-0.84,p=0.02)than trained individuals(ES=0.15,95%CI:-0.23 to 0.53,p=0.45).Interventions lasting a greater number of weeks(>7 weeks)had a larger ES(ES=0.47,95%CI:0.06-0.88,p=0.03)than those lasting fewer weeks(ES=0.22,95%CI:-0.12 to 0.55,p=0.20).Programmes with<2 sessions per week exhibited a larger ES(ES=0.39,95%CI:0.01-0.77,p=0.04)than programmes that incorporated>2 sessions per week(ES=0.20,95%CI:-0.10 to 0.50,p=0.18).Programmes with<250 jumps per week(ES=0.50,95%CI:0.02-0.97,p=0.04)showed a larger effect than programmes with250-500 jumps per week(ES=0.36,95%CI:0.00-0.72,p=0.05).Programmes with>500 jumps per week had negative effects(ES=-0.22,95%CI:-1.10 to 0.67,p=0.63).Programmes with>7.5 jumps per set showed larger effect sizes(ES=0.55,95%CI:0.02-1.08,p=0.04)than those with<7.5 jumps per set(ES=0.32,95%CI:0.01-0.62,p=0.04).Conclusion:PJT enhances lower-body stiffness,which can be optimised with lower volumes(<250 jumps per week)over a relatively long period of time(>7 weeks).

An Improved Jump Spider Optimization for Network Traffic Identification Feature Selection

The massive influx of traffic on the Internet has made the composition of web traffic increasingly complex.Traditional port-based or protocol-based network traffic identification methods are no longer suitable for today’s complex and changing networks.Recently,machine learning has beenwidely applied to network traffic recognition.Still,high-dimensional features and redundant data in network traffic can lead to slow convergence problems and low identification accuracy of network traffic recognition algorithms.Taking advantage of the faster optimizationseeking capability of the jumping spider optimization algorithm(JSOA),this paper proposes a jumping spider optimization algorithmthat incorporates the harris hawk optimization(HHO)and small hole imaging(HHJSOA).We use it in network traffic identification feature selection.First,the method incorporates the HHO escape energy factor and the hard siege strategy to forma newsearch strategy for HHJSOA.This location update strategy enhances the search range of the optimal solution of HHJSOA.We use small hole imaging to update the inferior individual.Next,the feature selection problem is coded to propose a jumping spiders individual coding scheme.Multiple iterations of the HHJSOA algorithmfind the optimal individual used as the selected feature for KNN classification.Finally,we validate the classification accuracy and performance of the HHJSOA algorithm using the UNSW-NB15 dataset and KDD99 dataset.Experimental results show that compared with other algorithms for the UNSW-NB15 dataset,the improvement is at least 0.0705,0.00147,and 1 on the accuracy,fitness value,and the number of features.In addition,compared with other feature selectionmethods for the same datasets,the proposed algorithmhas faster convergence,better merit-seeking,and robustness.Therefore,HHJSOAcan improve the classification accuracy and solve the problem that the network traffic recognition algorithm needs to be faster to converge and easily fall into local optimum due to high-dimensional features.

Upside and downside correlated jump risk premia of currency options and expected returns

This research explores upside and downside jumps in the dynamic processes of three rates:domestic interest rates,foreign interest rates,and exchange rates.To fill the gap between the asymmetric jump in the currency market and the current models,a correlated asymmetric jump model is proposed to capture the co-movement of the correlated jump risks for the three rates and identify the correlated jump risk premia.The likelihood ratio test results show that the new model performs best in 1-,3-,6-,and 12-month maturities.The in-and out-of-sample test results indicate that the new model can capture more risk factors with relatively small pricing errors.Finally,the risk factors captured by the new model can explain the exchange rate fluctuations for various economic events.

Finite-time H_(∞) filtering for Markov jump systems with uniform quantization

This paper is concerned with finite-time H_(∞) filtering for Markov jump systems with uniform quantization. The objective is to design quantized mode-dependent filters to ensure that the filtering error system is not only mean-square finite-time bounded but also has a prescribed finite-time H_(∞) performance. First, the case where the switching modes of the filter align with those of the MJS is considered. A numerically tractable filter design approach is proposed utilizing a mode-dependent Lyapunov function, Schur’s complement, and Dynkin’s formula. Then, the study is extended to a scenario where the switching modes of the filter can differ from those of the MJS. To address this situation, a mode-mismatched filter design approach is developed by leveraging a hidden Markov model to describe the asynchronous mode switching and the double expectation formula. Finally, a spring system model subject to a Markov chain is employed to validate the effectiveness of the quantized filter design approaches.

Stochastic Models to Mitigate Sparse Sensor Attacks in Continuous-Time Non-Linear Cyber-Physical Systems

Cyber-Physical Systems are very vulnerable to sparse sensor attacks.But current protection mechanisms employ linear and deterministic models which cannot detect attacks precisely.Therefore,in this paper,we propose a new non-linear generalized model to describe Cyber-Physical Systems.This model includes unknown multivariable discrete and continuous-time functions and different multiplicative noises to represent the evolution of physical processes and randomeffects in the physical and computationalworlds.Besides,the digitalization stage in hardware devices is represented too.Attackers and most critical sparse sensor attacks are described through a stochastic process.The reconstruction and protectionmechanisms are based on aweighted stochasticmodel.Error probability in data samples is estimated through different indicators commonly employed in non-linear dynamics(such as the Fourier transform,first-return maps,or the probability density function).A decision algorithm calculates the final reconstructed value considering the previous error probability.An experimental validation based on simulation tools and real deployments is also carried out.Both,the new technology performance and scalability are studied.Results prove that the proposed solution protects Cyber-Physical Systems against up to 92%of attacks and perturbations,with a computational delay below 2.5 s.The proposed model shows a linear complexity,as recursive or iterative structures are not employed,just algebraic and probabilistic functions.In conclusion,the new model and reconstructionmechanism can protect successfully Cyber-Physical Systems against sparse sensor attacks,even in dense or pervasive deployments and scenarios.

Experiments on two-phase flow in hydraulic jump on pebbled rough bed:Part 2–Bubble clustering

A survey on bubble clustering in air–water flow processes may provide significant insights into turbulent two-phaseflow.These processes have been studied in plunging jets,dropshafts,and hydraulic jumps on a smooth bed.As a first attempt,this study examined the bubble clustering process in hydraulic jumps on a pebbled rough bed using experimental data for 1.70<Fr_(1)<2.84(with Fr_(1) denoting the inflow Froude number).The basic properties of particle grouping and clustering,including the number of clusters,the dimensionless number of clusters per second,the percentage of clustered bubbles,and the number of bubbles per cluster,were analyzed based on two criteria.For both criteria,the maximum cluster count rate was greater on the rough bed than on the smooth bed,suggesting greater interactions between turbulence and bubbly flow on the rough bed.The results were consistent with the longitudinal distribution of the interfacial velocity using one of the criteria.In addition,the clustering process was analyzed using a different approach:the interparticle arrival time of bubbles.The comparison showed that the bubbly flow structure had a greater density of bubbles per unitflux on the rough bed than on the smooth bed.Bed roughness was the dominant parameter close to the jump toe.Further downstream,Fr_(1) predominated.Thus,the rate of bubble density decreased more rapidly for the hydraulic jump with the lowest Fr_(1).

Experiments on two-phase flow in hydraulic jump on pebbled rough bed:Part 1–Turbulence properties and particle chord time and length

This study reported and discussed turbulence characteristics,such as turbulence intensity,correlation time scales,and advective length scales.The characteristic air–water time scale,including the particle chord time and length and their probability density functions(PDFs),was investigated.The results demonstrated that turbulence intensity was relatively greater on a rough bed in the roller length,whereas further downstream,the decay rate was higher.In addition,the relationship between turbulence intensity and dimensionless bubble count rate reflected an increase in turbulence intensity associated with the number of entrained particles.Triple decomposition analysis(TDA)was performed to determine the contributions of slow and fast turbulent components.The TDA results indicated that,regardless of bed type and inflow conditions,the sum of the band-pass(T'_(u))and high-pass(T″_(u))filtered turbulence intensities was equal to the turbulence intensity of the raw signal data(T_(u)).T″_(u) highlighted a higher turbulence intensity and larger vorticities on the rough bed for an identical inflow Froude number.Additional TDA results were presented in terms of the interfacial velocity,auto-and cross-correlation time scales,and longitudinal advection length scale,with the effects of low-and high-frequency signal components on each highlighted parameter.The analysis of the air chord time indicated an increase in the proportion of small bubbles moving downstream.The second part of this research focused on the basic properties of particle grouping and clustering.

Noncooperative Model Predictive Game With Markov Jump Graph

In this paper,the distributed stochastic model predictive control(MPC)is proposed for the noncooperative game problem of the discrete-time multi-player systems(MPSs)with the undirected Markov jump graph.To reflect the reality,the state and input constraints have been considered along with the external disturbances.An iterative algorithm is designed such that model predictive noncooperative game could converge to the socalledε-Nash equilibrium in a distributed manner.Sufficient conditions are established to guarantee the convergence of the proposed algorithm.In addition,a set of easy-to-check conditions are provided to ensure the mean-square uniform bounded stability of the underlying MPSs.Finally,a numerical example on a group of spacecrafts is studied to verify the effectiveness of the proposed method.

Faster Than Sound, Daredevil Parachute Jumps from the Edge of the Space

This article discusses the kinematics of a parachutist making a very-high-altitude jump. The effect of altitude on the density of air, on the gravitational field strength of the Earth, and on the atmosphere’s temperature has been taken into account in our analysis. The well-known equations of classical mechanics governing the selected topic have been solved numerically by using the mathematical software Mathcad. Especially, the possibility of a person exceeding the speed of sound during their fall has been considered in our analysis. The effect of the sound barrier is taken into account so that the shape factor of the falling body is given as a speed-dependent function, which reaches its maximum value at Mach 1.0. The obtained results have been found to be highly consistent with the available experimental data on some high-altitude jumps. The data published on the famous jump of Captain Joseph Kittinger has been analyzed very carefully, and although our calculations reproduced the reported values for most parts, some interesting inconsistencies were also discovered. Kittinger jumped from a gondola attached to a helium-filled balloon from a record-high altitude of 102,800 ft, or 31,330 m, in August 1960. We also made numerical analysis on the high-altitude jump of Felix Baumgartner. He bailed out from his gondola at the record-high altitude of 39.0 km in October 2012.

Reachable set estimation for discrete-time Markovian jump neural networks with unified uncertain transition probability

This paper focuses on the reachable set estimation for Markovian jump neural networks with time delay.By allowing uncertainty in the transition probabilities,a framework unifies and enhances the generality and realism of these systems.To fully exploit the unified uncertain transition probabilities,an equivalent transformation technique is introduced as an alternative to traditional estimation methods,effectively utilizing the information of transition probabilities.Furthermore,a vector Wirtinger-based summation inequality is proposed,which captures more system information compared to existing ones.Building upon these components,a novel condition that guarantees a reachable set estimation is presented for Markovian jump neural networks with unified uncertain transition probabilities.A numerical example is illustrated to demonstrate the superiority of the approaches.